VAR, SVAR and SVEC Models: Implementation Within<i>R</i>Package<b>vars</b>

Pfaff, Bernhard

doi:10.18637/jss.v027.i04

Cited by 364 publications

(199 citation statements)

References 31 publications

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“…We set the lag order p equal to 2 after evaluating different VAR(p) models using Akaike's information criteria (Pfaff 2008a). For each VAR(2) model, we then tested the ability of each harvest variable (CPUE HZ1 and CPUE HZ2 ) to predict the other.…”

Section: Statistical Analysesmentioning

confidence: 99%

Evidence for a broad-scale decline in giant Australian cuttlefish (Sepia apama) abundance from non-targeted survey data

Prowse

Gillanders

Brook

et al. 2015

Mar. Freshwater Res.

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Little is known about the population trajectory and dynamics of many marine invertebrates because of a lack of robust observational data. The giant Australian cuttlefish (Sepia apama) is IUCN-listed as Near Threatened because the largest known breeding aggregation of this species in northern Spencer Gulf, South Australia, has declined markedly since the turn of the century. We used by-catch records from long-term trawl surveys to derive abundance data for S. apama and commercial cuttlefish harvest data as a measure of exploitation. Using Bayesian hierarchical models to account for zeroinflation and spatial dependence in these abundance counts, we demonstrated a high probability of broad-scale declines in the density of S. apama, particularly surrounding the primary aggregation site, which supports the recent closure of the entire S. apama fishery in northern Spencer Gulf. Historical harvest data were positively correlated with S. apama density estimated from the trawl surveys, suggesting that the commercial cuttlefish catch tracks the species abundance. Our results also indicated the possibility that the known S. apama breeding grounds might be supplemented by individuals that were spawned elsewhere in northern Spencer Gulf.

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Section: Statistical Analysesmentioning

confidence: 99%

Evidence for a broad-scale decline in giant Australian cuttlefish (Sepia apama) abundance from non-targeted survey data

Prowse

Gillanders

Brook

et al. 2015

Mar. Freshwater Res.

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“…However, if there is co-integration between the two variables, the resulting system will be stationary and as a result the test statistics will be asymptotically valid (Engle and Granger, 1987). I tested public support and legislative output for co-integration and the p-value of 0.037 provided by the Phillips-Ouliaris co-integration test (Pfaff, 2008) shows that the null hypothesis of no co-integration can be refuted. The implication of co-integration is that the series are drifting together at roughly the same rate (Greene, 2003: 650).…”

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confidence: 99%

Public opinion and policy output in the European Union: A lost relationship

Toshkov

2011

European Union Politics

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The European Union (EU) is assumed to suffer from a democratic deficit whereas there is only a weak and indirect connection between public preferences and policy change. This article investigates empirically whether any relationship exists between public support for European integration and EU policy output . Using a new indicator of policy output -the volume of important legislation produced in a semester -I discover a surprising relationship between public support and legislative production. Employing Vector autoregression (VAR) I demonstrate that public EU support Granger-causes legislative output but not vice versa, and that the relationship is strong up to the middle of the 1990s but non-existent afterwards. The effect is robust to the inclusion of indicators of the state of economy and government preferences. In addition, I discover that the average level of EU support in the Council of Ministers follows with a 4-year delay unemployment levels.

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“…pertain to the category of trend stationarity, because these variables are usually undergoing systematic change over the course of therapy. The VARMAX procedure implemented in SAS® version 9.3 (SAS Institute Inc., 2011) provides a simple option (trend=linear) for VAR-analyses of data that contain linear trends, and the same applies to the package vars (Pfaff, 2008;Pfaff & Stigler, 2013) (Tables W4 & W5). Besides the assumption of stationarity, time-series analysis requires that data were assessed at fixed intervals (equally spaced data).…”

Section: Running Head: Time Series Panel Analysis 13mentioning

confidence: 99%

Time-series panel analysis (TSPA): Multivariate modeling of temporal associations in psychotherapy process.

Ramseyer¹,

Kupper²,

Caspar³

et al. 2014

Journal of Consulting and Clinical Psychology

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Objective: Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce Time Series Panel Analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session, aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals, and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. Method:TSPA is based on vector autoregression (VAR), an extension of univariate autoregression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients that were monitored by post-session questionnaires.Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a second step, the associations between mechanisms of change (TSPA) and pre-to-post symptom-change were explored. Results: TSPA allowed identifying a prototypical process pattern, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery-and clarification interventions was positively associated with better outcome. Conclusions: TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. When asked to describe the mechanisms of therapeutic change in a patient, a psychotherapist will likely point to associations between certain factors of the therapist-patient system. These associations occur temporally and in sequence: "When I did X, the patient responded Y". A therapist relies on highly idiographic information, temporal evolution, and the action of multiple factors when evaluating therapy effectiveness. Yet, comparing these properties of therapists' reasoning to the strategies predominant in psychotherapy research (such as outcome research, process research), a stark contrast exists between a therapist's real-life complexity and research-dictated simplicity. This paper aims to show that the viewpoints and interests of the practitioner and the scientist need not be mutually exclusive, and that time-series (i.e. repeated measurements) may capture the dynamics of a psychotherapy process and vastly broaden the analytic and inferential possibilities. We present statistical methodology that accounts for temporal and individual-level information (idiographic perspective) and also generates predictions at a general or group level (nomothetic perspective). In the following application of Time Series P...

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VAR, SVAR and SVEC Models: Implementation WithinRPackagevars

Cited by 364 publications

References 31 publications

Evidence for a broad-scale decline in giant Australian cuttlefish (Sepia apama) abundance from non-targeted survey data

Evidence for a broad-scale decline in giant Australian cuttlefish (Sepia apama) abundance from non-targeted survey data

Public opinion and policy output in the European Union: A lost relationship

Time-series panel analysis (TSPA): Multivariate modeling of temporal associations in psychotherapy process.

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